WaveGuard

waveguard_mechanism_probe

Read-onlyIdempotent

Run targeted interventions and rank effect sizes.

Input Schema

TableJSON Schema

Name	Required	Description
`training`	Yes	2+ baseline normal samples used to construct the reference profile.
`base_test`	Yes	Baseline candidate sample before interventions.
`field_level`	No	0 = real scalar field, 1 = complex field.
`sensitivity`	No	Anomaly sensitivity multiplier (default: 1.0).
`encoder_type`	No	Optional encoder override. Omit to auto-detect.
`intervention_tests`	Yes	1+ intervention variants used to estimate effect sizes.
`intervention_labels`	No	Optional labels for intervention variants (same order as intervention_tests).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedJul 28, 2026

TDQS

B3.1/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, indicating a safe, non-destructive operation. The description 'Run targeted interventions' does not contradict these, but adds no new behavioral context beyond what annotations provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence with no waste, achieving conciseness. However, it could benefit from brevity without sacrificing clarity; front-loading the core action is effective.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 7 parameters, no output schema, and a complex domain (mechanism probing, effect sizes), the description is too brief. It fails to explain what 'rank effect sizes' means, the return format, or how to interpret results, leaving gaps for the agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% with clear parameter descriptions (e.g., '2+ baseline normal samples used to construct the reference profile'). The tool description adds no additional meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Run targeted interventions and rank effect sizes' clearly indicates the tool performs interventions and ranks effects, distinguishing it from sibling tools like 'waveguard_scan' or 'waveguard_health' which lack this focus. However, it could be more specific about what 'targeted interventions' entail.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance is provided on when to use this tool versus alternatives like 'waveguard_counterfactual' or 'waveguard_interaction_matrix'. The description lacks when-to-use, when-not-to-use, or prerequisite information.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.9/5.0

Disambiguation5/5

Each tool has a clear, distinct purpose. While tools like waveguard_scan and waveguard_scan_timeseries both detect anomalies, their descriptions clearly differentiate structured data from time-series data. Similarly, token risk, volume check, and price manipulation tools address separate aspects of token analysis, avoiding overlap.

Naming Consistency5/5

All tools follow the consistent pattern 'waveguard_<descriptive_name>' in snake_case. The names are descriptive and predictable, making it easy to infer functionality from the name alone. No mixed conventions or vague names are present.

Tool Count4/5

19 tools is on the higher side but still appropriate given the broad domain of crypto analytics, anomaly detection, and market data. Each tool serves a specific purpose, and the count is justified by the comprehensive feature set. However, a few tools could potentially be merged without loss of clarity.

Completeness4/5

The tool set covers a wide range of functionalities: health check, market data fetching, fingerprinting, similarity comparison, anomaly detection (structured and time-series), risk assessment (token, price manipulation, volume, wallet), counterfactuals, and interaction matrices. Some minor gaps exist, such as missing data export or visualization tools, but core workflows are well-supported.

Resources

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